ライフサイエンスコーパス: gene prediction

1 ul genome analysis depends on the quality of gene prediction.

2 ay probe selection to de novo non-coding RNA gene prediction.

3 rious computing techniques are available for gene prediction.

4 ful and general approach for microRNA target gene prediction.

5 s is a useful and general approach for miRNA gene prediction.

6 del implementations for ab initio eukaryotic gene prediction.

7 rotein sequences of related genes to aid the gene prediction.

8 ction missed by conventional mutagenesis and gene prediction.

9 le, powerful way to increase the accuracy of gene prediction.

10 become a practical and powerful strategy for gene prediction.

11 was isolated, and its sequence confirmed the gene prediction.

12 icantly more accurate than GeneMark in exact gene prediction.

13 gnition programs to increase the accuracy of gene prediction.

14 designing a new combined tool for automatic gene prediction.

15 y of model reconstruction and, subsequently, gene prediction.

16 pecific HMMs that are able to offer unbiased gene predictions.

17 additional criteria to refine many existing gene predictions.

18 logs in Homo for 82% (15,250) of Monodelphis gene predictions.

19 an 20% would have been detected by ab initio gene predictions.

20 ally combine multiple probe predictions into gene predictions.

21 standing tool that identifies such erroneous gene predictions.

22 or exons, leading to biologically irrelevant gene predictions.

23 validation together with gradually improving gene predictions.

24 y of using RT-PCR as a method for confirming gene predictions.

25 luding MEGF1, G3BP, and several of the novel gene predictions.

26 raining and subsequently generates ab initio gene predictions.

27 den input/output Markov models for combining gene predictions.

28 logy studies, domain searches, and ab initio gene predictions.

29 y in mutation rates on false-positive driver gene predictions.

30 d that GeneWise provided reasonably accurate gene predictions.

31 heir highly reduced sizes and their reliable gene predictions.

32 e the quality and biological context of tRNA gene predictions.

33 portal for interactive exploration of these gene predictions.

34 tegrates RNA-Seq read information into final gene predictions.

35 ssembly scaffolds, including eight annotated gene predictions.

36 ials and few species-specific protein-coding gene predictions.

37 equence information and to verify or improve gene predictions.

38 class are a valuable source of evidence for gene predictions.

39 tructure of genes can improve the quality of gene predictions.

40 nown genes, JIGSAW can substantially improve gene prediction accuracy as compared with existing metho

41 at this combined approach appears to improve gene prediction accuracy compared with current methods t

42 Gene prediction accuracy improves steadily from 1X throu

43 This experiment establishes a baseline of gene prediction accuracy in Caenorhabditis genomes, and

44 odels with cross-species comparison improves gene prediction accuracy.

45 annotation to generate and modify ab initio gene predictions across the whole genome.

46 se of mass spectrometry to improve automated gene prediction, adding 800 correct exons to our predict

47 upported the intron-exon boundaries of their gene predictions, adding only 5'- and 3'-untranslated re

48 m are generated by GeneMark-ES, an ab initio gene prediction algorithm based on unsupervised training

49 in coding by an MCMV-specific version of the gene prediction algorithm GeneMarkS.

50 The TWINSCAN gene prediction algorithm was adapted for the fungal pat

51 Using the GENSCAN gene prediction algorithm, we generated predicted transc

52 y runs are used to automatically retrain its gene-prediction algorithm, producing higher-quality gene

53 formance of 780 distinct classifiers (set of genes + prediction algorithm) in full cross-validation.

54 Gene prediction algorithms (or gene callers) are an esse

55 edictions remains challenging: even the best gene prediction algorithms make substantial errors and c

56 Computational gene prediction algorithms were used to query the sequen

57 time and has provided the basis for powerful gene prediction algorithms, its origins are still not fu

58 gene boundaries from three different protein gene prediction algorithms, tRNAscan-SE gene predictions

59 iption units and new genes not identified by gene prediction algorithms.

60 e to detect novel exons not predicted by any gene prediction algorithms.

61 ne level) needs additional improvement using gene prediction algorithms.

62 Gene annotation is the final goal of gene prediction algorithms.

63 s, which had been undetected by conventional gene-prediction algorithms, are identified by the codon-

64 ase homology searches, and computer-assisted gene prediction analyses.

65 results of EST database searches and GENSCAN gene prediction analysis reveal 13 serine protease genes

66 e leave-one-out cross-validation and de novo gene prediction analysis, our approach achieved the area

67 lysis of microarrays) and minimal subsets of genes (prediction analysis for microarrays) that succinc

68 Our analysis used gene prediction and a database search of 430 kb of genom

69 order to achieve fast and reliable automatic gene prediction and annotation.

70 We have combined computational gene prediction and comparative sequence analysis to cha

71 mber of bioinformatics tools that facilitate gene prediction and cross- species comparisons.

72 for genome annotation, structural genomics, gene prediction and domain-based genomic studies.

73 The GeneWise method for combining gene prediction and homology searches was applied to the

74 sufficient to allow map-based cross-species gene prediction and isolation.

75 petitive elements is a key step for accurate gene prediction and overall structural annotation of gen

76 gene prediction improves substantially when gene prediction and pseudogene masking are interleaved.

77 First, 16S rRNA gene prediction and the inclusion of ultrafast exact ali

78 The gene predictions and accompanying functional assignments

79 Public access to our RNA gene predictions and an interface for user predictions i

80 pproach to validate and refine computational gene predictions and define full-length transcripts on t

81 e exons, and thus aid in the construction of gene predictions and elucidation of alternative splicing

82 Target gene predictions and experimental verification indicate

83 'guessing' PCR primers on top of unreliable gene predictions and frequently leads to wasting of expe

84 gastric niche and demonstrate that in silico gene predictions and in vitro tests have limitations for

85 The resulting report identifies problematic gene predictions and includes extensive statistics and g

86 (OPTIC) database currently provides sets of gene predictions and orthology assignments for three cla

87 embly in the context of externally available gene predictions and other features.

88 g expression-based validation for 84% of the gene predictions and providing clues as to the functions

89 r (GV) to automatically identify problematic gene predictions and to aid manual curation.

90 oducts from five DEFL clusters confirmed our gene predictions and verified expression.

91 the region containing KRIT1/Krit1 using exon/gene-prediction and comparative alignment programs revea

92 and genetic maps, genome sequence assembly, gene prediction, and integration of EST data.

93 Combining RT-PCR, in silico gene prediction, and paralogy analysis, we can identify

94 EST alignments, ab initio gene prediction, and sequence similarity searches of the

95 tially sequenced genome, mostly by in silico gene prediction, and there has been no major improvement

96 and proteins to a genome, produces ab initio gene predictions, and automatically synthesizes these da

97 uction, protein-based anchoring of ab-initio gene predictions, and constraints derived from a global

98 d PPFINDER to remove pseudogenes from N-SCAN gene predictions, and show that gene prediction improves

99 Our gene prediction approach prioritized druggable genes tha

100 Correct gene predictions are crucial for most analyses of genome

101 Problematic gene predictions are flagged and can be reannotated usin

102 n of our results suggests that some nematode gene predictions are incorrect, leading to erroneous pai

103 Consensus gene predictions are then derived by maximum likelihood

104 on of long genomic sequences and comparative gene prediction as recently pointed out by Zhang et al.

105 In addition, we used PPFINDER with gene predictions as a parent database, eliminating the n

106 featuring transcript alignments to validate gene predictions as well as motif and similarity analyse

107 SMCRFs are a promising framework for gene prediction because of their highly modular nature,

108 ur models within each cluster on the initial gene predictions before making final predictions.

109 on and homology analyses to produce reliable gene predictions but they often fail to detect many actu

110 tions should be regularly checked to improve gene prediction by sequence similarity and greater effor

111 Our approach was to produce gene predictions by algorithms that rely on comparative

112 ture of the method is the ability to enhance gene predictions by finding the best alignment between t

113 ral, these results showed that computational gene prediction can be a reliable tool for annotating ne

114 y insufficient for assembly, and traditional gene prediction cannot be applied to unassembled short r

115 le cDNA and expressed sequence tag data with gene predictions, clarifying single nucleotide polymorph

116 w GC content can make both better and unique gene predictions compared to gene prediction programs th

117 ows that Seqping was able to generate better gene predictions compared to three HMM-based programs (M

118 encing in combination with RACE on ab initio gene predictions could be used to define the transcripto

119 t the initiation rate and, in the context of gene prediction, could reduce the accuracy of the identi

120 expressed sequence tag alignments, multiple gene predictions, cross-species homologies, single nucle

121 isive for discriminating between alternative gene predictions derived from computational sequence ins

122 expression profiles indicate that 5% of the gene predictions encode mRNAs that are found only in the

123 However, exact gene prediction (especially at the gene level) needs add

124 Our results show that combining gene prediction evidence consistently outperforms even t

125 VIGOR produces four output files: a gene prediction file, a complementary DNA file, an align

126 l Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an aut

127 We are able to rank gene predictions for a significant portion of the diseas

128 The gene predictions for human can be browsed and downloaded

129 trees of species and comparisons of several gene predictions for sensitivity and specificity in find

130 ently make mistakes and therefore the use of gene predictions for sequence annotation is hardly possi

131 The gene predictions for these viruses have been evaluated b

132 pecifically, M-BISON increases the AUC of DE gene prediction from .541 to .623 when compared to a met

133 nput a genomic sequence and the locations of gene predictions from ab initio gene finders, protein se

134 igation indicates that many of the incorrect gene predictions from GeneWise were due to transposons w

135 f Takifugu Toll-like receptor (TLR) loci and gene predictions from many draft genomes enable comprehe

136 e a Bayesian network framework for combining gene predictions from multiple systems.

137 ouse genome features generated by distilling gene predictions from NCBI, Ensembl and VEGA.

138 iocurators and researchers who need accurate gene predictions from newly sequenced genomes.

139 ents and multi-genome alignments, as well as gene predictions from other gene-finders.

140 a sets, the inclusion of genome sequence and gene predictions from related species and active literat

141 and eight other eukaryote model genomes, and gene predictions from several groups.

142 s, Exegesis was used to process the original gene predictions from the automated Ensembl annotation p

143 steps in microbial genome analysis-assembly, gene prediction, functional annotation-in a way that all

144 f solution exists for the combined assembly, gene prediction, genome annotation and data presentation

145 atter of debate; the number of computational gene predictions greatly exceeds the number of validated

146 Since the release of WS9, gene predictions have improved continuously.

147 9B, and CrSUMO-like90) were found by diverse gene predictions, hidden Markov models, and database sea

148 We report a strategy of focused candidate gene prediction, high-throughput sequencing and experime

149 We present 'gene prediction improvement pipeline' (GenePRIMP), a com

150 from N-SCAN gene predictions, and show that gene prediction improves substantially when gene predict

151 Accurate ab initio gene prediction in a short nucleotide sequence of anonym

152 at this protocol will also be beneficial for gene prediction in any organism with bimodal or other un

153 U, has been developed for plant miRNA target gene prediction in any plant, if a large number of seque

154 mpletely sequenced bacterial genes, accurate gene prediction in bacterial genomes remains an importan

155 We present three programs for ab initio gene prediction in eukaryotes: Exonomy, Unveil and Glimm

156 n of SMCRFs advances the state of the art in gene prediction in fungi and provides a robust platform

157 We show that TWINSCAN improves gene prediction in human using intermediate products fro

158 Gene prediction in metagenomic sequences remains a diffi

159 d for miRNA and precursor mining, and target gene prediction in non-model plants.

160 halum using several different approaches for gene prediction in organisms with insertional RNA editin

161 rk family of programs designed and tuned for gene prediction in prokaryotic, eukaryotic and viral gen

162 We show improved performance of essential gene prediction in the bacterium Yersinia pestis, the ca

163 ent program to allow for rapid alignment and gene prediction in user submitted sequences.

164 this activity, we identified several hundred gene predictions in mouse with varying levels of support

165 Existing computational methods for gene prediction include ab initio methods which use the

166 Gene prediction is a ubiquitous step in sequence analysi

167 se results underscore the fact that metazoan gene prediction is a very challenging task and that most

168 Correct gene prediction is impaired by the presence of processed

169 Gene prediction is one of the most important steps in th

170 However, in the absence of transcript data, gene prediction is still challenging.

171 We have developed a novel gene prediction method FragGeneScan, which combines sequ

172 pathogenic viruses, we combined a new miRNA gene prediction method with small-RNA cloning from sever

173 The new gene prediction method, called GeneMarkS, utilizes a non

174 evaluated commonly used lines of evidence in gene prediction methodologies, and investigated patterns

175 rences in the genomic sequences or different gene prediction methodologies, we analyzed both genome a

176 the assemblies and especially the different gene-prediction methodologies.

177 se-negative rate, lower than most of current gene prediction methods and a false-positive rate lower

178 nome analysis and the limitations of current gene prediction methods and understanding.

179 Computational gene prediction methods are an important component of wh

180 f MRIII was analysed using the computational gene prediction methods NIX and TAP to identify putative

181 Thus, gene prediction methods that explicitly take into accoun

182 he genome sequence was analyzed with several gene prediction methods to produce a comprehensive gene

183 method is also more accurate than ab initio gene prediction methods, provided sufficiently close tar

184 tion framework that can be applied to driver gene prediction methods.

185 n be used in combination with other existing gene-prediction methods.

186 rom healthy human brain to develop a disease gene prediction model and this generic methodology can b

187 Gene prediction modeling identified 34,809 genes, with m

188 Gene predictions, mRNA alignments, epigenomic data from

189 egrates assembly data, comparative genomics, gene predictions, mRNA and EST alignments and physiologi

190 mosome and includes assembly data, genes and gene predictions, mRNA and EST alignments, and comparati

191 sembly data, sequence composition, genes and gene predictions, mRNA and expressed sequence tag eviden

192 Gene predictions, multiple alignments and phylogenetic t

193 ave made another approximately 10,000-20,000 gene predictions of lower confidence, supported by vario

194 We investigated the accuracy of gene prediction on different levels and designed several

195 Gene predictions on the assembled sequence suggest that

196 erent workers, from a bioinformatician doing gene prediction or a bench scientist designing primers f

197 en made to integrate pseudogene removal with gene prediction, or even to provide a freestanding tool

198 ave been integrated into WormBase, including gene predictions, ortholog assignments and a new synteny

199 y improved performance of ChIP-BIT in target gene prediction, particularly for detecting weak binding

200 bled' RNA-Seq reads improves the accuracy of gene prediction; particularly, for the 1.3 GB genome of

201 on the CoGenT++ environment include disease gene prediction, pattern discovery, automated domain det

202 We present an automated gene prediction pipeline, Seqping that uses self-trainin

203 that integration of such information into a gene-prediction pipeline is feasible and doing so may im

204 tive analysis tools, such as genes missed by gene prediction pipelines or genes without an associated

205 This allows us to run the gene prediction/PPFINDER procedure on newly sequenced ge

206 ncorporation of additional evidence into the gene prediction process, we show how it can be used to b

207 s with the syntenic human locus at 13q22 and gene prediction program analysis, we found a single clus

208 The output of any external gene prediction program can be easily converted to a gen

209 The Ensembl gene prediction program has been used for the complete g

210 t MetaProdigal, a metagenomic version of the gene prediction program Prodigal, that can identify gene

211 BLAST searches using a gene prediction program revealed that 86% of TNP flankin

212 We present here a homology-based gene prediction program to accurately predict small pept

213 A gene prediction program, VIGOR (Viral Genome ORF Reader)

214 ould be obtained by creating a more accurate gene-prediction program and then amplifying and sequenci

215 Annotation tools such as optimized gene prediction programs are being developed for rice to

216 Training data for gene prediction programs are often chosen randomly from

217 When gene prediction programs are trained on a subset of gras

218 Gene prediction programs frequently mistake processed ps

219 hich allows users to query multiple exon and gene prediction programs in an automated fashion.

220 ne pipeline to several widely used ab initio gene prediction programs in rice; this comparison shows

221 The main challenge in combination of gene prediction programs is the fact that the systems ar

222 Gene prediction programs offer an efficient way to gener

223 better performance than all general-purpose gene prediction programs surveyed.

224 the lowest false-positive rate of the coding gene prediction programs tested, and Infernal has a low

225 tter and unique gene predictions compared to gene prediction programs that are trained on genes with

226 We show that gene prediction programs that are trained with grass gen

227 We find that gene prediction programs trained on grass genes with ran

228 By separately training gene prediction programs with genes from multiple GC ran

229 Using gene prediction programs, a further 19 potential genes a

230 sed or assigned incorrect start positions by gene prediction programs, and suggest corrections to imp

231 were identified by homology searches and by gene prediction programs, and their gene structures and

232 tructural annotation depends on well-trained gene prediction programs.

233 tilizing homology to improve the accuracy of gene prediction programs.

234 lly but not completely with predictions from gene prediction programs.

235 combining predictions from already existing gene prediction programs.

236 plice groups, which can be incorporated into gene prediction programs.

237 major groups, which can be incorporated into gene prediction programs.

238 proximal false positives be incorporated in gene prediction programs.

239 d, analyzed for homologies, and subjected to gene prediction programs.

240 P genes were analysed with a set of exon and gene prediction programs.

241 m presented here was designed to improve the gene prediction quality in terms of finding exact gene b

242 In addition, RGD provides tools for gene prediction, radiation hybrid mapping, polymorphic m

243 mapped to all organisms, RefSeq alignments, gene predictions, regulatory elements, gene expression d

244 Existing methods for ncRNA gene prediction rely mostly on homology information, thu

245 However, obtaining accurate gene predictions remains challenging: even the best gene

246 dies of molecular evolution and to assist in gene prediction research, we have constructed an Exon-In

247 In this Perspective, I review the state of gene prediction roughly 10 years ago, summarize the prog

248 The increased gene prediction sensitivity results in part from novel s

249 an assembly, hg38/GRCh38, to include updated gene prediction sets from GENCODE, more phenotype- and d

250 compared with the introns of other ab initio gene prediction sets.

251 tively assess the accuracy of protein-coding gene prediction software in C. elegans, and to apply thi

252 Most current gene prediction software, whether based on ab initio or

253 roach is developed that largely improves the gene prediction specificity.

254 e the supervised model training precedes the gene prediction step.

255 identification which bypasses the unreliable gene prediction step.

256 a widely used phenotype database in disease gene prediction studies.

257 In this work, we developed a metagenomics gene prediction system Glimmer-MG that achieves signific

258 Our approach was to adapt the TWINSCAN gene prediction system to C. elegans and C. briggsae and

259 tical, and is suitable for use in a combined gene prediction system where other methods identify well

260 TWINSCAN is a gene-prediction system that combines the methods of sing

261 and implemented them in the TWINSCAN/N-SCAN gene-prediction system.

262 as accelerated, the need for highly accurate gene prediction systems has grown.

263 orporating multiple sources of evidence into gene prediction systems.

264 However, popular similarity search tools and gene prediction techniques generally fail to identify mo

265 The steady improvements in gene prediction that have occurred over the last 10 year

266 predicted by comparative mapping, indicated gene predictions that are likely to be incorrect, and id

267 Exegesis is a procedure to refine the gene predictions that are produced for complex genomes,

268 used to validate novel UTR introns in human gene predictions that do not overlap any RefSeq gene and

269 We have assembled a collection of >10,000 gene predictions that do not overlap existing gene annot

270 of the human genome sequence, with confirmed gene predictions that have been integrated with external

271 to predict new genes, including two GenScan gene predictions that overlapped ESTs and were shown to

272 thin this region, which contains 255 Ensembl gene predictions, the aligned sequences clustered into 5

273 ith known model organism gene homologies and gene predictions to provided basic comparative data.

274 GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into

275 While gene prediction tools have similar accuracies predicting

276 ers" in proteomics, improves on the existing gene prediction tools in genomics, and allows identifica

277 Computational gene prediction tools routinely generate large volumes o

278 in FragGeneScan is more sensitive than other gene prediction tools, while Prodigal, MGA, and MGM are

279 addition to the narrow arsenal of automatic gene prediction tools.

280 Some of the best methods for gene prediction use either sequence composition analysis

281 tational methodologies for miRNA:mRNA target gene prediction use the seed segment of the miRNA and re

282 nd Gramene allow BarleyBase users to perform gene predictions using the 21,439 non-redundant Barley1

283 pipeline predictions are more accurate than gene predictions using the other three approaches with t

284 ost computational methodologies for microRNA gene prediction utilize techniques based on sequence con

285 Insertional RNA editing renders gene prediction very difficult compared to organisms wit

286 Among 23,021 gene predictions we identified 0.2% strong candidates fo

287 that may not satisfy existing heuristics for gene prediction, we developed a computational and experi

288 Using de novo gene prediction, we identified 6,996 protein-encoding ge

289 Applying this method to the human Ensembl gene predictions, we discovered that 2011 (9% of total)

290 In testing experimentally unsupported gene predictions, we were able to identify 58 that are n

291 Ab initio gene predictions were selected for high-throughput valid

292 61 (46%) overlapped 184 (72%) of the Ensembl gene predictions, whereas 307 were unique to Incyte.

293 Our studies combining computational gene prediction with genetic and comparative genomic ana

294 ogress, there is a growing need to integrate gene prediction with metabolic network analysis.

295 The suggested method of parallelization of gene prediction with the model parameters estimation fol

296 This program provides rRNA gene predictions with high sensitivity and specificity o

297 has been established to validate novel human gene predictions with no prior experimental evidence of

298 tein gene prediction algorithms, tRNAscan-SE gene predictions with RNA secondary structures and CRISP

299 ession profiles of sequences found in 35,282 gene predictions within the sea urchin genome.

300 on of the same genome, derived from distinct gene prediction workflows.